Graduate (List)

This is a graduate level course on data communication. The first half of the course involves an overview, data transmission, and data communication network. The latter half of the course involves internet protocol, internet service, and wireless internet.

In this course, based on the fundamental concepts and knowledge addressed in EE210, we discuss advanced topics in probability and random processes for applications in engineering. Topics include algebra of sets, limit events, random vectors, convergence, correlation functions, independent increment processes, and compound processes. (Prerequisite: {EE210} or {Approval of the Instructor})

This course, as an advanced course of EE 421, aims at providing a strong foundation for research in the are of wireless communications. The course will address (1) wireless channel models from a system theoretic viewpoint, (2) modulation and demodulation in wireless channels, (3) coding techniques for wireless channels, (4) various equalization techniques for ISI channels, (5) multicarrier transmission techniques including OFDM, (6) spread spectrum technique (DS, FH), and (7) MIMO communications with a focus on single-user MIMO.

Introduce students the fundamental concepts and intuition behind modern machine learning techniques and algorithms, beginning with topics such as perceptron to more recent topics such as boosting, support vector machines and Bayesian networks. Statistical inference will be the foundation for most of the algorithms covered in the course.

This course will discuss the key differences in architecture and algorithms between conventional information processing systems (e.g. von Neumann machines) and biological brains. Subsequently, we will try to come up with the scaffold of a basic design for a non-von Neumann type of brain-like information processing system.

This course explains how digital signal processing techniques can be applied in the field of speech communication. The initial part of the course covers some background material in signal processing and the acoustic theory of speech production. Later lectures cover coding, recognition, and synthesis of speech. (Prerequisite: EE202)

This course handles underlying background theories for pattern recognition (PR) which is the start point for AI. It covers PR systems, Bayesian Classifier, likelihood-based PR, Discriminant Function-based PR, Support Vector Machine, NN-based PR, and other PR theories such as fuzzy theory, and so on.

This course deals with the fundamental concept of digital image processing, analysis, and understanding. Topics include sampling, linear and nonlinear operations of images, image compression, enhancement and restoration, reconstruction from projections, feature extraction, and image understanding.

This course covers the theory and application of neural networks. In particular, lectures explore the structure and function of neural networks and their learning and generalization. Also, various models of neural networks and their applications are illustrated.

This course is to allow the students majoring in the general areas of communications and signal processing (and those in other areas also) to obtain the basic and advanced knowledge of statistical techniques for signal processing. Topics include multivariate distributions, order statistics, and their applications. The key concepts, theory, and methodology of nonlinear techniques for statistical signal processing are studied.
(Prerequisite: EE528 recommended)